import joblib
import json
import numpy as np

# Load model
model = joblib.load('isolation_forest.pkl')

# Load feature names
with open('features.json', 'r') as f:
    feature_names = json.load(f)

def predict(inputs):
    """Run inference on input data."""
    # Handle single input or batch
    if isinstance(inputs, dict):
        inputs = [inputs]
    
    # Extract features in correct order
    features = []
    for input_dict in inputs:
        feature_vector = [input_dict.get(feat, 0) for feat in feature_names]
        features.append(feature_vector)
    
    # Convert to numpy array
    X = np.array(features)
    
    # Get anomaly scores
    scores = model.decision_function(X)
    
    # Normalize to 0-1 scale (higher = more anomalous)
    normalized_scores = (0.5 - scores) / 1.0
    normalized_scores = np.clip(normalized_scores, 0, 1)
    
    # Return as list
    return normalized_scores.tolist()

# For Hugging Face Inference API
def handler(event, context):
    """Handler for Hugging Face Inference API."""
    inputs = event.get('inputs', event)
    scores = predict(inputs)
    return {"score": scores[0] if len(scores) == 1 else scores}